Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T5/00—Image enhancement or restoration
  • G06T5/90—Dynamic range modification of images or parts thereof
  • G06T5/92—Dynamic range modification of images or parts thereof based on global image properties
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T5/00—Image enhancement or restoration
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
  • H04N1/40—Picture signal circuits
  • H04N1/407—Control or modification of tonal gradation or of extreme levels, e.g. background level
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N5/00—Details of television systems
  • H04N5/14—Picture signal circuitry for video frequency region
  • H04N5/20—Circuitry for controlling amplitude response
  • H04N5/202—Gamma control
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10016—Video; Image sequence
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20004—Adaptive image processing
  • G06T2207/20012—Locally adaptive
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20172—Image enhancement details
  • G06T2207/20208—High dynamic range [HDR] image processing

Definitions

• Many capture device for example scanners or digital cameras, capture images as a two dimensional array of pixeis. Each pixel will have associated intensity values in a predefined color space, for example red, green and blue.
• the intensity values may be captured using a high bit depth for each color, for example 12 or 16 bits deep.
• the captured intensity values are typically linearly spaced.
• the intensity values of each color When saved as a final image, or displayed on a display screen, the intensity values of each color may be converted to a lower bit depth with a non-iinear spacing, for example 8 bits per color.
• a final image with 8 bits per color (with three colors) may be represented as a 24 bit color image. Mapping the linear high bit depth image (12 or 16 bits per color) into the lower nonlinear bit depth image (8 bits per color) is typically done using a gamma correction tone map.
• Multi-projector systems often require high-bit depth to prevent contouring in the blend areas (the blends must vary smoothly). This becomes a much more significant issue when correcting black offsets digitally since a discrete digital jump from 0 to I does not allow a representation of continuous values in that range. Also, in a display system the "blends" or s ⁇ bframe values are often computed in linear space with high precision (16-bit) and then gamma corrected to 8 non-iinear bits.
• FIG. 1 is a two dimensional array of intensity values representing a small part of an image, in an example embodiment of the invention.
• FIG.2 is a tabic showing the mapping of the intensity values of a linear 4 bit image into the intensity values of a non-linear 2 bit image with a gamma of 2.2.
• FlG. 3 shows the image from figure 1 after having been mapped into a 2 bit
• FIG. 4 is a flow chart showing a method for combining gamma correction with dithering in an example embodiment of the invention.
• FIG. Sa is a table showing the intensity values of the high bit depth image in an example embodiment of the invention.
• Sb is a table showing the intensity values of the lower bit depth image in non-iinear space and in linear space, in an example embodiment of the invention.
• FIG.6 is a dither pattern in an example embodiment of the invention.
• FIG. 7 is a small image, in an example embodiment of the invention.
• FIG. 8 is a table that lists the results for overlaying the dither pattern in figure 6 onto the small image of figure 7, in an example embodiment of the invention.
• FIG. 9 is a final image in an example embodiment of the invention.
• FIG. 10 is a block diagram of a computer system 1000 in an example embodiment of the invention.
• FIGS, i - 10 and the following description depict specific examples to teach those skilled in the art how to make and use the best mode of the invention. For the purpose of teaching inventive principles, some conventional aspects have been simplified or omitted. Those skilled in the art will appreciate variations from these examples that fall within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. As a result, the invention is not limited to the specific examples described below, but only by the claims and their equivalents.
• mapping an image from a high bit depth linear image into a lower bit depth non-linear image can be done over many different bit depth levels. For example mappings may be done from 16 bits (65,536 levels) to 8 bits (256 levels), from 12 bit to S bite, from S bits to 4 bits, from 4 bits into 2 bits, or the like.
• each intensity level in the high bit depth image is first normalized to between 0 and 1.
• each color channel is processed independently. Normalization is done by dividing the original intensity value by the largest possible intensity value for the current bit depth.
• Normalized Non-linear Value (Normalized Value) ⁇ ( I /gamma) Equation 1
• the normalized Non-linear intensity value is given by raising die normalized intensity value to one over the gamma value. For a gamma of 2.2, the normalized intensity value would be raised to the power of 1/2.2 or 0.4545. The original intensity value of 50 would yield a normalized mapped value of 0.4812
• Figure 1 is a two dimensional array of intensity values representing a small part of an image, in an example embodiment of the invention.
• the image in figure 1 is a 4 bit image with intensity values ranging from 0 - 15.
• Figure 2 is a table showing the mapping of the intensity values of a linear 4 bit image into the intensity values of a nonlinear 2 bit image with a gamma of 2.2.
• Figure 3 shows the image from figure 1 after having been mapped into a 2 bit (4 level) space using a 2.2 gamma mapping.
• Figure 3 may have visible banding between the 3 different levels.
• a dithering step is combined with the mapping step to produce an image that may show less contouring.
• Figure 4 is a flow chart showing a method for combining gamma correction with dithering in an example embodiment of the invention. Using the method shown in figure 4, a high bit depth linear image is represented using a smaller number of non-linear levels where the smaller number of non-linear levels are spatially modulated across the final image.
• each intensity value in the high bit depth linear image is mapped to an intensity value in the non-linear space, hi one example embodiment of the invention, the mapping is done using gamma correction. In other example embodiments of the invention, other mapping algorithms may be used.
• a left and right interval boundary is calculated for each of the intensity values in nonlinear space. Once the left and right interval boundaries are calculated, they are mapped into linear space.
• a dither pattern is overlaid onto the pixels of the original image in linear space.
• the intensity value at each pixel is snapped to one of the two closest left and right interval boundaries in linear space, based on the original linear intensity value, the left and right interval boundary values (in linear space), and the value of the dither screen at that pixel location.
• the non-linear gamma corrected intensity value for the pixel location is determined.
• FIG. 7 The first step is to map each intensity value in the high bit depth linear image to an intensity value in the nonlinear space.
• Equation i is used for mapping from a linear image to a non-linear image when the mapping is done using a gamma correction function.
• Figure Sa is a table showing the intensity values of the high bit depth image in an example embodiment of the invention.
• the first column in figure 5a lists the normalized intensity values in 4 bit linear space.
• the second column in figure Sa lists the normalized intensity values in non-linear space.
• Each intensity value in column 2 was generated using equation 1 with a 2.2 gamma correction.
• the gamma corrected value for intensity value 2 (in non-linear space) is generated by first normalizing the 4 bit value, and then raising that normalized value to the power of 1/2.2 resulting in a value of 0.40017 ( (0. 13333 > ⁇ ⁇ 3/2.2) ⁇ 0.40017).
• the next step is to generate the left and right boundary intervals for each high bit depth intensity value.
• the left and right boundary intervals represent the two closest lower bit depth non-linear intensity values to the current non-linear intensity value. Equations 2 and 3 are used to calculate the left and right boundary intervals respectively.
• IntensityVal is the normalized high bit depth intensity value in non- liner space
• MaxIV is the maximum low bit depth intensity value
• intergerValue is a function that truncates any fractional value (i.e. it converts a floating point value into an integer value).
• the first step in equation 1 [integerValue(intensityVal * MaxIV) ] takes the normalized high bit depth intensity value and multiplies it by the maximum quantized low bit depth intensity value. The result is converted from a floating point value into an integer. This converts the normalized high bit depth intensity value into a lower bit depth intensity value.
• the second step in equation 1 normalizes the lower bit depth value to between zero and one by dividing by the maximum low bit depth intensity value. The calculation for the left boundary interval value in non-linear space for the 4 bit intensity value of 6 is shown below. Left ⁇ ((integerValue(0.65935• 3))/3) Left ⁇ ((integerValueO .97805))/3) Left ⁇ (1/3) Left ⁇ 0.33333
• FIG. 5b is a table showing the intensity values of the lower bit depth image in non-linear space and in linear space, in an example embodiment of the invention.
• the first column in figure 5b lists the intensity values of the lower bit depth image in non-linear space.
• the second column in table 5b lists the intensity values of the lower bit depth image in linear space.
• a dither pattern is overlaid onto the pixels of the original image in linear space.
• a dither pattern may be a matrix of threshold intensity values, a single threshold intensity value with a pattern for propagating error to other pixels, a single threshold with a pattern of noise addition, or the like.
• the dither pattern is shown in figure 6. Any type of dither pattern may be used, including error diffusion or random noise injection. The size of the dither pattern may also be varied.
• the dither pattern shown in figure 6 is a 4x4 Bayer dither pattern. Before the dither pattern is overlaid onto the intensity values in the original image, the intensity values in the dither pattern are normalized to a value between 0 and 1.
• the intensity value at each pixel is snapped to one of the two closest left and right interval boundaries in linear space, based on the original linear intensity value, the left and right interval boundary values in linear space, and the value of the dither screen at that pixel location.
• the correct left or right interval boundary is selected using equations 4 and 5.
• IntensityN is the original high bit depth linear intensity value for the current pixel normalized to between 0 and 1
• left and right are the left and right boundary intervals in linear space for the current intensity value
• Dither is the normalized dither value for the current pixel.
• CompVal is set to zero when the expression is false and CompVal is set to one when the expression is true.
• SelectedVal will equal the right value when CompVal is one, and will equal the left value when CompVal is a zero.
• Figure 7 is a small section of an image, in an example embodiment of the invention.
• Figure S is a table that lists the results for overlaying the dither pattern in figure 6 onto the small image of figure 7, in an example embodiment of the invention.
• the first column in figure 8 lists the pixel location in the image.
• the second column lists the normalized intensity value of the image for each pixel location.
• the third and fourth columns list the left and right boundary intervals in linear space for each pixel location, respectively.
• the fifth column lists the normalized dither pattern value for each pixel location.
• the sixth column lists the calculated CompVal for each pixel location.
• the last column lists the SelectedVal for each pixel location.
• Equations 4 and 5 are used to calculate the last two columns in figure 8.
• the last step is to map the selected value from the linear space to the nonlinear space. This can be done using a lookup table.
• the lookup table in figure Sb is used tor this example.
• Figure 9 is the final image from the example above.
• the image can be saved or stored onto a computer readable medium.
• a computer readable medium can comprise the following: random access memory, read only memory, hard drives, tapes, optical disk drives, non-volatile ram, video ram, and the like.
• the image can be used in many ways, for example displayed on one or more displays, transferred to other storage devices, or the like.
• Computer system has a processor 1002, a memory device 1004, a storage device 1006, a display 1008, and an I/O device 1010.
• the processor 1002, memory device 1004, storage device 1006, display device 1008 and I/O device 1010 are coupled together with bus 1012.
• Processor 1002 is configured to execute computer instruction that implement the method describe above.

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• Computer Vision & Pattern Recognition (AREA)
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• Facsimile Image Signal Circuits (AREA)

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• 2009
  • 2009-07-30 JP JP2012522787A patent/JP2013500677A/ja active Pending
  • 2009-07-30 WO PCT/US2009/052226 patent/WO2011014170A1/en active Application Filing
  • 2009-07-30 CN CN2009801606961A patent/CN102473289A/zh active Pending
  • 2009-07-30 US US13/258,563 patent/US20120014594A1/en not_active Abandoned
  • 2009-07-30 EP EP09847913A patent/EP2411962A4/en not_active Withdrawn
  • 2009-07-30 KR KR1020117027076A patent/KR20120046103A/ko not_active Application Discontinuation
• 2010
  • 2010-07-27 TW TW099124715A patent/TW201106295A/zh unknown

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